Quickstart

There are four basic steps to build an end-to-end flow on Context Data

1). Source Connection: Build connection(s) to where your source data resides (e.g. MySQL, PostgreSQL, Amazon S3)

2). Embedding Model: Create a link to the embedding model which will convert data retrieved from the source to vector embeddings (basically an array of numbers)

3). Target Connection: Build connection(s) to where the vector embeddings will be saved (and where your AI application will read from)

4). Flow: The flow ties of the steps above (source connection, embedding model and target connection) into an end-to-end process ready to be executed.

Basically, when a flow is triggered, it will:

• Get the data from the source connection that you defined

• Convert the retrieved data to a format optimized for vector search

• Write the converted data to the vector database/store